This section is intended to introduce the reader to various aspects of art, which may be related to various aspects of the present invention that are described and/or claimed below. This discussion is believed to be helpful in providing the reader with background information to facilitate a better understanding of the various aspects of the present invention. Accordingly, it should be understood that these statements are to be read in this light, and not as admissions of prior art.
There are situations where both a user of a home network and the user's Internet Service Provider (ISP) want to agree on the estimation of the network traffic, i.e. the data consumed by the user. Such situations encompass, but are not restricted to, proof of fair network use and verification of the consumption in a pay-per-use model.
According to a prior art solution, the ISP first by its own means logs the network traffic generated by each user and then analyzes and consolidates logs to obtain an estimation. This solution works reasonably well, but it has a number drawbacks: i) the price to provide a precise estimation is high; and ii) the user may refute the estimation, especially if it is not precise.
A more recent solution comes from the domain of anti-piracy of copyrighted content. The user voluntarily installs “spy” software on each networked device. The installed software controls the download behaviour of the device, for example through the use of a white list of authorized content, a white list of authorized web sites, a white list of authorized protocols, black lists or any other suitable technical means. The installed software also performs a local estimation of the device's network consumption. As the estimation is local, it has good chances of being precise and relatively cheap, particularly for the ISP as it uses resources on the user side. Whenever the user wants to prove the network consumption, the sum of the estimations of all pieces of installed software is sent to the ISP or any other entity that wishes to verify the network traffic.
This solution has one major drawback: it can be easily attacked by confining the installed spy software to virtual machines that voluntary have a very low network activity, thus resulting in a low or even null local estimation of network traffic.
The skilled person will thus appreciate that network traffic control software is pertinent only if all the traffic in the network is analyzed. A problem is to gain assurance that all the traffic is analyzed.
A different system is described by R. Poortinga et al. in “Analysing Campus Traffic Using the Meter-MIB”, Proceedings of the Passive and Active Measurements Workshop (PAM 2002), Fort Collins, Colo., USA. The document describes a network with user devices connected to switches and then, possibly, to an outside network. The switches measure the traffic for each user device in order to see how good a measure the switches came up with when it came to outgoing traffic. To this end, a meter PC was put on the outgoing line to measure the outgoing traffic only. As it turned out, there is very little relation between the two measurements, so no reliable estimation may be made.
It will thus be appreciated that there is a need for a cost efficient solution that can provide an estimation of the network consumption that overcomes the attack using virtual machines. The present invention provides such a solution.